Method for assessing the treatment of attention-deficit/hyperactivity disorder

Abstract

According to one aspect, there is provided a method for assessing the treatment of attention-deficit/hyperactivity disorder (ADHD) in a subject, the method comprising: obtaining electroencephalographic (EEG) data relating to a plurality of subjects diagnosed with ADHD; extracting, for each of the plurality of subjects, at least one feature from the EEG data relating to that subject; formulating a prediction model by performing regression analysis to map the extracted features against one or more markers for each of the plurality of subjects; and determining that the prediction model provides an ADHD assessment if one or more of the markers are indicators of a clinical measure of interest.

1 . A method for assessing the treatment of attention-deficit/hyperactivity disorder (ADHD) in a subject, the method comprising:
 obtaining electroencephalographic (EEG) data relating to a plurality of subjects diagnosed with ADHD;   extracting, for each of the plurality of subjects, at least one feature from the EEG data relating to that subject;   formulating a prediction model by performing regression analysis to map the extracted features against one or more markers for each of the plurality of subjects; and   determining that the prediction model provides an ADHD assessment if one or more of the markers are indicators of a clinical measure of interest.   
     
     
         2 . The method of  claim 1 , further comprising retrieving clinically obtained ADHD scores relating to the plurality of subjects; and using the clinically obtained ADHD scores as input to the regression analysis, to formulate the prediction model. 
     
     
         3 . The method of  claim 1 , wherein the EEG data comprises historical EEG data relating to the plurality of subjects or is obtained from a brain-computer-interface device. 
     
     
         4 . The method of  claim 1 , wherein the at least one feature is a discriminative rhythmic feature. 
     
     
         5 . The method of  claim 4 , wherein the discriminative rhythmic feature is extracted using a complex-valued spatial-spectral filtering technique. 
     
     
         6 . The method of  claim 5 , wherein the complex-valued spatial-spectral filtering technique uses an adapted form of Rayleigh coefficient as an objective function for system optimization. 
     
     
         7 . The method of  claim 5 , wherein the complex-valued spatial-spectral filtering technique is a linear-phase technique. 
     
     
         8 . The method of  claim 7 , wherein the linear-phase complex-valued spatial-spectral filtering technique comprises:
 transforming a segment of the EEG data into the frequency domain;   computing a spatial energy feature at each of a number of given frequencies using a linear-phase complex-valued spatial-spectral filer and spectral coefficients of the EEG data segment; and   summing computed spatial energy features over a range of frequency points of interest to obtain a spatial-spectral power feature.   
     
     
         9 . The method of  claim 8 , wherein the spatial energy feature is computed according to the following expressions:
     y   f   =w   f   x   f   y   f        and       y   f   =w   f   x   f   w   f      where y f  is the spatial energy feature at a given frequency f; W f  is the linear-phase complex-valued spatial-spectral filter; X f  are spectral coefficients of the EEG data segment; and T denotes a matrix transposition.   
     
     
         10 . The method of  claim 1 , wherein the prediction model that is formulated depends on the extracted features that are mapped for the formulated prediction model to provide a different ADHD assessment score. 
     
     
         11 . The method of  claim 1 , wherein the prediction model used to obtain each of the ADHD assessment is different. 
     
     
         12 . The method of  claim 1 , wherein the prediction model used to obtain a first of the ADHD assessment is based on the prediction model used to obtain a second of the ADHD assessment. 
     
     
         13 . The method of  claim 1 , wherein the ADHD assessment is an ADHD severity score at an instance of obtaining the EEG data for one subject. 
     
     
         14 . The method of  claim 1 , wherein the ADHD assessment is a change score between two time points for one subject. 
     
     
         15 . The method of  claim 12 , wherein the two time points are two instances of obtaining the EEG data for one subject, to provide an ADHD response score. 
     
     
         16 . The method of  claim 12 , wherein the two time points are at an instance of obtaining the EEG data for one subject and another instance where the EEG data has yet to be obtained, to provide an ADHD predictor score. 
     
     
         17 . The method of  claim 1 , wherein the ADHD assessment comprises:
 an ADHD severity score at an instance of obtaining the EEG data for one subject; and   a change score between two time points for one subject, wherein the two time points are
 two instances of obtaining the EEG data for the one subject, to provide an ADHD response score; or 
 an instance of obtaining the EEG data for the one subject and another instance where the EEG data has yet to be obtained, to provide an ADHD predictor score,
 wherein the prediction model used to obtain the ADHD severity score, the ADHD response score and the ADHD predictor score is different. 
 
   
     
     
         18 . The method of  claim 1 , wherein the ADHD assessment comprises:
 an ADHD severity score at an instance of obtaining the EEG data for one subject; and   a change score between two time points for one subject, wherein the two time points are
 two instances of obtaining the EEG data for the one subject, to provide an ADHD response score; or 
 an instance of obtaining the EEG data for the one subject and another instance where the EEG data has yet to be obtained, to provide an ADHD predictor score,
 wherein the prediction model used to obtain the ADHD response score and the ADHD predictor score is based on the prediction model used to obtain the ADHD severity score. 
 
   
     
     
         19 . An apparatus for assessing the treatment of attention-deficit/hyperactivity disorder (ADHD) in a subject, the apparatus comprising:
 at least one processor; and   at least one memory including computer program code, the at least one memory and the computer program code configured to, with the at least one processor, cause the apparatus at least to perform:
 obtaining electroencephalographic (EEG) data relating to a plurality of subjects diagnosed with ADHD; 
 extracting, for each, of the plurality of subjects, at least one feature from the EEG data relating to that subject; 
 formulating a prediction model by performing regression analysis to map the extracted features against one or more markers for each of the plurality of subjects; and 
 determining that the prediction model provides an ADHD assessment if one or more of the markers are indicators of a clinical measure of interest. 
   
     
     
         20 . A computer readable medium for assessing the treatment of attention-deficit/hyperactivity disorder (ADHD) in a subject, the computer readable medium having stored thereon computer program code which when executed by a computer causes the computer to perform at least the following:
 obtaining electroencephalographic (EEG) data relating to a plurality of subjects diagnosed with ADHD;   extracting, for each of the plurality of subjects, at least one feature from the EEG data relating to that subject;   formulating a prediction model by performing regression analysis to map the extracted features against one or more markers for each of the plurality of subjects; and   determining that the prediction model provides an ADHD assessment if one or more of the markers are indicators of a clinical measure of interest.